Machine Learning – The Primer
A series of posts that I have
written around Machine learning. This covers all relevant topics covered under
the gamut of Machine learning. The posts are broken into 5 blogs - excerpts and
links to each one are provided below.
In this series of posts, I will emphasize primarily over the world of Machine learning. We’ll start with an overview of how machine learning models work and how they are used. This may feel basic if you’ve done statistical modeling or machine learning before. By giving ‘computers the ability to learn’, we mean passing the task of optimization — of weighing the variables in the available data to make accurate predictions about the future — to the algorithm.
We are going to continue to learn more about the steps in machine learning process and how it can be enhanced and made more efficient. There is a huge significance to be on top of Domain knowledge in your said industry/process. This will filter the right data set to be considered for machine learning. The core of this discussion goes around Data and its structure that exists in your organization.
We are going to continue to learn more about the tools/techniques and hardware that will be required for these processes to excel. While the majority of us are appreciating the early applications of machine learning, it continues to evolve at quite a promising pace, introducing us to more advanced algorithms like Deep Learning. Why DL is so good? It is simply great in terms of accuracy when trained with a huge amount of data. Also, it plays a significant role to fill the gap when a scenario is challenging for the human brain. So, quite logical this contributed to a whole slew of new frameworks appearing. Please see below the top 10 frameworks that are available and their intended use in the relevant industry space.
We are going to dwell a little deeper into the Algorithms that are at the core of the ML/DL space and how humans are at the helm of this impact of machines. Algorithms are becoming an integral part of our daily lives. About 80% of the viewing hours on Netflix and 35% of the retail on Amazon are due to automated recommendations by the so called AI/ML engines. Designers in companies like Facebook know how to make use of notifications and gamifications to increase user engagement and exploit & amplify various human vulnerabilities such as social approval and instant gratification. In short, we are nudged and sometimes even tricked into making choices by algorithms that need to learn. In case of the products and services we buy, the information we consume and who we mingle with online, algorithms are playing an important role in practically every aspect of it.
We will discuss the applications/organizations who have successfully implemented the AI/ML frameworks and how they are benefiting out of it. Have you ever thought how Google Maps predicts the traffic so accurately or how Amazon recommends products for you or even how self-driving cars work? If yes, then let us see the top 8 applications of Machine Learning.
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